A sequential pattern classifier based on hidden Markov kernel machine and its application to phoneme classification

Yotaro Kubo, Shinji Watanabe, Atsushi Nakamura, Erik McDermott, Tetsunori Kobayashi

    研究成果: Article

    7 引用 (Scopus)

    抄録

    This paper describes a novel classifier for sequential data based on nonlinear classification derived from kernel methods. In the proposed method, kernel methods are used for enhancing the emission probability density functions (pdfs) of hidden Markov models (HMMs). Because the emission pdfs enhanced by kernel methods have sufficient nonlinear classification performance, mixture models such as Gaussian mixture models (GMMs), which might cause problems of overfitting and local optima, are not necessary in the proposed method. Unlike the methods used in earlier studies on sequential pattern classification using kernel methods, our method can be regarded as an extension of conventional HMMs, and therefore, it can completely model the transition of hidden states with the observed vectors. Therefore, our method can be applied to many applications developed with conventional HMMs, especially for speech recognition. In this paper, we carried out an isolated phoneme classification as a preliminary experiment in order to evaluate the efficiency of the proposed sequential pattern classifier. We confirmed that the proposed method achieved steady improvements as compared to conventional HMMs with Gaussian-mixture emission pdfs trained by the maximum likelihood and the maximum mutual information procedures.

    元の言語English
    記事番号5570878
    ページ(範囲)974-984
    ページ数11
    ジャーナルIEEE Journal on Selected Topics in Signal Processing
    4
    発行部数6
    DOI
    出版物ステータスPublished - 2010 12

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    Hidden Markov models
    Classifiers
    Probability density function
    Speech recognition
    Maximum likelihood
    Pattern recognition
    Experiments

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Signal Processing

    これを引用

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    abstract = "This paper describes a novel classifier for sequential data based on nonlinear classification derived from kernel methods. In the proposed method, kernel methods are used for enhancing the emission probability density functions (pdfs) of hidden Markov models (HMMs). Because the emission pdfs enhanced by kernel methods have sufficient nonlinear classification performance, mixture models such as Gaussian mixture models (GMMs), which might cause problems of overfitting and local optima, are not necessary in the proposed method. Unlike the methods used in earlier studies on sequential pattern classification using kernel methods, our method can be regarded as an extension of conventional HMMs, and therefore, it can completely model the transition of hidden states with the observed vectors. Therefore, our method can be applied to many applications developed with conventional HMMs, especially for speech recognition. In this paper, we carried out an isolated phoneme classification as a preliminary experiment in order to evaluate the efficiency of the proposed sequential pattern classifier. We confirmed that the proposed method achieved steady improvements as compared to conventional HMMs with Gaussian-mixture emission pdfs trained by the maximum likelihood and the maximum mutual information procedures.",
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    AU - Watanabe, Shinji

    AU - Nakamura, Atsushi

    AU - McDermott, Erik

    AU - Kobayashi, Tetsunori

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